Method of analyzing a medical image

ABSTRACT

A method of analyzing a medical image, where the medical image comprises one or more than one region of interest, and where the method comprises a) providing the medical image comprising a set of actual image values; b) rescaling the actual image values to produce corresponding rescaled image values and to produce a rescaled image from the rescaled image values; c) deriving a histogram of the rescaled image values; d) using the histogram to derive an adaptive segmentation threshold; e) using the adaptive segmentation threshold to recursively split the rescaled image; f) terminating the recursive splitting of the sub(sub) images using one or more than one predetermined criteria; and g) identifying one sub(sub) image in the terminated Hierarchical Region Splitting Tree which comprises the region of interest.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is a continuation of U.S. patent applicationSer. No. 13/580,947 titled “Method of Analyzing a Medical Image,” filedAug. 24, 2012, which is a national stage of International PatentApplication No. PCT/US2011/025943, titled “Method of Analyzing a MedicalImage,” filed Feb. 23, 2011, which claims the benefit of U.S.Provisional Patent Application No. 61/307,396 entitled “Method forDistinguishing Normal from Abnormal Tissue,” filed Feb. 23, 2010; andU.S. Provisional Patent Application No. 61/327,630 entitled “Method ofAnalyzing a Medical Image,” filed Apr. 23, 2010, the contents of whichare incorporated in this disclosure by reference in their entirety.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

This invention was made with United States Government support underCooperative Agreement Number DAMD17-97-2-7016 with the National MedicalTest-Bed, Inc., United States Army Medical Research Acquisition Activity(USAMRAA). The United States Government has certain rights in thisinvention.

BACKGROUND

Medical imaging, such as for example computed tomography scans (CTscan), magnetic resonance images (MRI), positron emission tomographyscans (PET scan) and X-rays are essential for diagnosing and formonitoring the treatment of patients. The gold standard for analyzing amedical image is by human visual inspection and analysis of the image bya trained technician; however, human visual inspection and analysis ofthe image usually takes from minutes to hours to complete. Often, timeis critical in reading a medical image and supplying the results to thepersonnel treating the patient.

Therefore, there is a need for a method of analyzing a medical imagethat provides similar results as human visual inspection and analysis ofthe image by a trained technician that takes substantially less timethan human visual inspection and analysis of the image by a trainedtechnician.

SUMMARY

According to one embodiment of the present invention, there is provideda method of analyzing a medical image, where the medical image comprisesone or more than one region of interest, and where the method comprisesa) providing the medical image comprising a set of actual image values;b) rescaling the actual image values to produce corresponding rescaledimage values and to produce a rescaled image from the rescaled imagevalues; c) deriving a histogram of the rescaled image values; d) usingthe histogram to derive an adaptive segmentation threshold that can beused to split the rescaled image into two sub-images, a first sub-imagewith intensities at or below the adaptive segmentation threshold and asecond sub-image with intensities above the adaptive segmentationthreshold, or a first sub-image with intensities below the adaptivesegmentation threshold and a second sub-image with intensities at orabove the adaptive segmentation threshold, or a first sub-image withintensities below the adaptive segmentation threshold and a secondsub-image with intensities above the adaptive segmentation threshold; e)using the adaptive segmentation threshold to recursively split therescaled image to generate a Hierarchical Region Splitting Tree ofsub(sub) images based on consistency of the rescaled image values of therescaled image; f) terminating the recursive splitting of the sub(sub)images using one or more than one predetermined criteria therebycompleting the Hierarchical Region Splitting Tree; and g) identifyingone sub(sub) image in the terminated Hierarchical Region Splitting Treewhich comprises the region of interest.

According to another embodiment of the present invention, there isprovided a method of analyzing a medical image, where the medical imagecomprises one or more than one region of interest. The method comprisesa) configuring at least one processor to perform the functions of: 1)providing the medical image comprising a set of actual image values; 2)rescaling the actual image values to produce corresponding rescaledimage values and to produce a rescaled image from the rescaled imagevalues; 3) deriving a histogram of the rescaled image values; 4) usingthe histogram to derive an adaptive segmentation threshold that can beused to split the rescaled image into two sub-images, a first sub-imagewith intensities at or below the adaptive segmentation threshold and asecond sub-image with intensities above the adaptive segmentationthreshold, or a first sub-image with intensities below the adaptivesegmentation threshold and a second sub-image with intensities at orabove the adaptive segmentation threshold; 5) using the adaptivesegmentation threshold to recursively split the rescaled image togenerate a Hierarchical Region Splitting Tree of sub(sub) images basedon consistency of the rescaled image values of the rescaled image; 6)terminating the recursive splitting of the sub(sub) images using one ormore than one predetermined criteria thereby completing the HierarchicalRegion Splitting Tree; and 7) identifying one sub(sub) image in theterminated Hierarchical Region Splitting Tree which comprises the regionof interest.

In one embodiment, the method further comprises performing a secondaryrescaling of the rescaled image values of every rescaled sub(sub) imagein the Hierarchical Region Splitting Tree back to the actual imagevalues present in the medical image to create a secondary rescaledmedical image, thereby producing a secondarily rescaled sub(sub) imagecomprising the region of interest. In another embodiment, the onesub(sub) image in the terminated Hierarchical Region Splitting Treecomprising the region of interest is two-dimensional. In anotherembodiment, the secondarily rescaled sub(sub) image in the terminatedHierarchical Region Splitting Tree comprising the region of interest isthree-dimensional. In a preferred embodiment, the secondarily rescaledone sub(sub) image in the terminated Hierarchical Region Splitting Treecomprising the region of interest is two-dimensional. In a preferredembodiment, the one sub(sub) image in the terminated Hierarchical RegionSplitting Tree comprising the region of interest is three-dimensional.In one embodiment, the medical image is selected from the groupconsisting of a computed tomography scan, a magnetic resonance image, apositron emission tomography scan and an X-ray. In one embodiment, theone sub(sub) image in the terminated Hierarchical Region Splitting Treecomprising the region of interest is two-dimensional. In one embodiment,the one sub(sub) image in the terminated Hierarchical Region SplittingTree comprising the region of interest is three-dimensional. In oneembodiment, the region of interest is a representation of an injury toliving human tissue and the method detects the representation of theinjury. In another embodiment, the method qualifies the representationof the injury. In one embodiment, the medical image is of human tissueis selected from the group consisting of brain, heart, intestines,joints, kidneys, liver, lungs and spleen. In another embodiment, themedical image provided is in a hard copy form, and where the methodfurther comprises preparing a digital form of the medical image beforeproviding the medical image. In one embodiment, the rescaled imagevalues fit in [0,255] unsigned 8-bit integer range. In anotherembodiment, the predetermined criteria are selected from the groupconsisting of area threshold=50 pixels and (standard deviationthreshold=10 rscVals (StdDevTh=10 rscVals) and kurtosis threshold=1.5).

In one embodiment, there is provided a method of detecting anabnormality in living human tissue. The method comprises analyzing amedical image according to the present invention, where the region ofinterest is a representation of the abnormality in the living humantissue, and where the method further comprises quantifying theabnormality in the living human tissue. In one embodiment, theabnormality is selected from the group consisting of a geneticmalformation and an injury. In one embodiment, the method furthercomprises performing a secondary rescaling of the rescaled image valuesin every rescaled sub(sub) image in the Hierarchical Region SplittingTree back to the actual image values present in the medical image tocreate a secondary rescaled medical image; and where the method furthercomprises determining an image value or a set of image values of actualimage values in the medical image after the secondary rescaling, wherethe image value or a set of image values of actual image valuesdetermined identifies the abnormality represented in the medical imagefor the modality being used to generate the medical image provided. Inone embodiment, determining the image value or a set of image values ofactual image values is made before the step of providing the medicalimage. In another embodiment, determining the image value or a set ofimage values of actual image values is made after the step of providingthe medical image. In one embodiment, the medical image is a magneticresonance image and the modality being used to generate the medicalimage provided is selected from the group consisting of an apparentdiffusion coefficient map, a magnetic susceptibility map and a T2 map.In another embodiment, the method further comprises preparing a mask ofthe sub(sub) image containing the representation of the abnormality, andcleaning the mask to remove small outlier regions to generate a cleanedmask of the sub(sub) image containing the representation of theabnormality.

In one embodiment, there is provided a method of detecting a core of aninjury and detecting a penumbra of an injury in living human tissue, anddistinguishing the core from the penumbra, the method comprising: a)detecting one sub(sub) image (the injury sub-image) in the terminatedHierarchical Region Splitting Tree comprising the region of interest,where the region of interest represents the injury according to thepresent invention; b) determining the mask of the injury; c) determininga sub-tree below the detected injury sub-image using the injurysub-image as the root of the sub-tree; d) determining the soft thresholdimage values for separating the core from the penumbra; e) comparing thesoft threshold image values inside the sub-tree to find the penumbra anda mask of the penumbra; and f) determining the mask of the core bysubtracting the mask of the penumbra from the mask of the injury. In oneembodiment, the method further comprises determining differentgradations of the core and the penumbra.

In one embodiment, the method further comprises quantifying thespatiotemporal evolution of an injury in living human tissue.

In another embodiment, there is provided a method of detecting theeffects of endogenous or implanted stem cells on living human tissue.The method comprises a) determining magnetic resonance image values oflabeled and implanted stem cells; b) detecting the stem cells outside ofthe region of interest using a method according to the presentinvention; and c) detecting the stem cells inside of the region ofinterest using a method according to the present invention. In oneembodiment, the method further comprises quantifying spatiotemporalactivities of implanted labeled stem cells in the living human tissue.

According to another embodiment of the present invention, there isprovided a method of detecting a core of an injury and detecting apenumbra of an injury in living human tissue, and distinguishing thecore from the penumbra. The method comprises a) configuring at least oneprocessor to perform the functions of: 1) detecting one sub(sub) image(the injury sub-image) in the terminated Hierarchical Region SplittingTree comprising the region of interest, where the region of interestrepresents the injury according to the present invention; 2) determiningthe mask of the injury; 3) determining a sub-tree below the detectedinjury sub-image using the injury sub-image as the root of the sub-tree;4) determining the soft threshold image values for separating the corefrom the penumbra; 5) comparing the soft threshold image values insidethe sub-tree to find the penumbra and a mask of the penumbra; and 6)determining the mask of the core by subtracting the mask of the penumbrafrom the mask of the injury. In one embodiment, the method furthercomprises determining different gradations of the core and the penumbra.In another embodiment, the method further comprises quantifying thespatiotemporal evolution of an injury in living human tissue.

According to one embodiment of the present invention, there is provideda method of detecting the effects of endogenous or implanted stem cellson living human tissue. The method comprises a) configuring at least oneprocessor to perform the functions of: 1) determining magnetic resonanceimage values of labeled and implanted stem cells; 2) detecting the stemcells outside of the region of interest using a method according thepresent invention; and 3) detecting the stem cells inside of the regionof interest using a method according to the present invention. In oneembodiment, the method further comprises quantifying spatiotemporalactivities of implanted labeled stem cells in the living human tissue.

According to another embodiment of the present invention, there isprovided a system for analyzing a medical image, where the medical imagecomprises one or more than one region of interest. The system comprisesa) one or more than one processor; b) a machine readable storageconnected to the one or more than one processor; c) a medical imagecomprising a set of actual image values stored in the storage; d) a setof machine readable instructions stored in the machine readable storageand operable on the medical image; e) a user interface operablyconnected to the set of computer instructions for transmitting one ormore than one command to the one or more than one processor; f)instructions operably connected to the user interface for rescaling theactual image values to produce corresponding rescaled image values andto produce a rescaled image from the rescaled image values; g)instructions operably connected to the user interface for deriving ahistogram of the rescaled image values; h) instructions operablyconnected to the user interface for using the histogram to derive anadaptive segmentation threshold that can be used to split the rescaledimage into two sub-images, a first sub-image with intensities at orbelow the adaptive segmentation threshold and a second sub-image withintensities above the adaptive segmentation threshold, or a firstsub-image with intensities below the adaptive segmentation threshold anda second sub-image with intensities at or above the adaptivesegmentation threshold; i) instructions operably connected to the userinterface for using the adaptive segmentation threshold to recursivelysplit the rescaled image to generate a Hierarchical Region SplittingTree of sub(sub) images based on consistency of the rescaled imagevalues of the rescaled image; j) instructions operably connected to theuser interface for terminating the recursive splitting of the sub(sub)images using one or more than one predetermined criteria therebycompleting the Hierarchical Region Splitting Tree; k) instructionsoperably connected to the user interface for identifying one sub(sub)image in the Hierarchical Region Splitting Tree comprising the region ofinterest; and l) a storage operably connected to the one or more thanone processor and the user interface for storing the resultantHierarchical Region Splitting Tree images.

DRAWINGS

These and other features, aspects and advantages of the presentinvention will become better understood with regard to the followingdescription, appended claims, and accompanying drawings where:

FIGS. 1A and 1B are block diagrams showing some steps in a methodaccording to the present invention of analyzing a medical imageaccording to the present invention;

FIG. 2 is a histogram of a rescaled apparent diffusion coefficient imageplotting rescaled apparent diffusion coefficient (ADC) image values inthe rescaled apparent diffusion coefficient image on the x axis (in thiscase apparent diffusion coefficient image values) versus frequency ofeach rescaled image value in the rescaled image on the y axis;

FIG. 3 is a corresponding table of rescaled apparent diffusioncoefficient image values for the rescaled image showing the adaptivesegmentation threshold (Th) found for the histogram in FIG. 2;

FIG. 4 is a schematic showing nomenclature rules for the recursivesplitting of a rescaled image;

FIG. 5 is a diagram of the top levels of a Hierarchical Region SplittingTree generated by the method of the present invention for a magneticresonance image which was generated from the histogram of FIG. 2, wherethe level of image splitting is shown on the right side of the diagrambeginning with the first image, Image 0, and the number of eachsub-image and the range of rescaled image values (v) present in thesub-image are shown below each sub-image;

FIG. 6 is a block diagram showing some steps in another method accordingto the present invention of detecting an abnormality in living humantissue using a magnetic resonance image as an example;

FIG. 7 and FIG. 8 show a histogram (left) of a rescaled image (plottingapparent diffusion coefficient rescaled image values in the rescaledimage on the x axis versus frequency of each rescaled image value in therescaled image on the y axis) and the part of the corresponding diagram(right) (Level 0, Level 1 and Level 3) of the levels of a HierarchicalRegion Splitting Tree generated by the method of the present inventionfor a magnetic resonance image which was generated from the histogram(left), where the level of image splitting is shown on the right side ofthe diagram beginning with the first image, Image 0, and the range ofrescaled image values present in the sub-image are shown below eachsub-image, where FIG. 6 is generated from the brain of a human neonatalpatient with a mild ischemic injury, and FIG. 7 is generated from thebrain of a human neonatal patient with a severe ischemic injury;

FIG. 9 and FIG. 10 show a histogram (left) of a rescaled image (plottingT2 relaxation time rescaled image values in the rescaled image on the xaxis versus frequency of each rescaled image value in the rescaled imageon the y axis) and the part of the corresponding diagram (right) (Level0, Level 1 and Level 2) of the levels of a Hierarchical Region SplittingTree generated by the method of the present invention for a magneticresonance image which was generated from the histogram (left), where thelevel of image splitting is shown on the right side of the diagrambeginning with the first image, Image 0, and the range of rescaled imagevalues present in the sub-image are shown below each sub-image, whereFIG. 9 is generated from a rat brain with a mild ischemic injury, andFIG. 10 is generated from a rat brain with a severe ischemic injury;

FIG. 11 is a diagram comparing volumetric results of magnetic resonanceimages of injured brains using methods according to the presentinvention (HRS) and using standard manual methods at differentinjury-severities;

FIG. 12 is a diagram of core-penumbra injury detected according to thepresent invention in an animal brain;

FIG. 13 is a diagram of core-penumbra injury detected according to thepresent invention in a human neonatal brain; and

FIG. 14 is a diagram depicting the detection of iron-labeled stem cellsin an ischemic animal brain over four weeks, where red is the ischemiclesion, yellow is the iron-labeled murine neuronal stem cells.

DESCRIPTION

According to one embodiment of the present invention, there is provideda method of analyzing a medical image comprising a region of interest,such as for example a medical image selected from the group consistingof a computed tomography scan (CT scan), a magnetic resonance image(MRI), a positron emission tomography scan (PET scan) and an X-ray. Inone embodiment, the region of interest is a representation of anabnormality of living human tissue, such as for example an injury toliving human tissue, and the method detects the representation of theabnormality. In a preferred embodiment, the method additionallyqualifies the region of interest. In another embodiment, there isprovided a method of detecting a core of an injury and detecting apenumbra of an abnormality of living human tissue, such as for examplean injury to living human tissue, and distinguishing the core from thepenumbra. According to one embodiment of the present invention, there isprovided a method of quantifying the spatiotemporal evolution of anabnormality of living human tissue, such as for example an injury toliving human tissue. According to another embodiment of the presentinvention, there is provided a method of detecting the effects ofendogenous or implanted neuronal stem cells (NSCs) on living humantissue. In a preferred embodiment, the living human tissue is a humanbrain.

The present method is an automated computational method referred to as“Hierarchical Region Splitting” (HRS). Using a magnetic resonance imageas an example of a medical image, Hierarchical Region Splittingadvantageously analyzes a magnetic resonance image approximately 100times faster than analyzing the magnetic resonance image by visualinspection and analysis of the image by a trained technician which arethe gold standard of analyzing magnetic resonance images. Alsoadvantageously, Hierarchical Region Splitting can analyze medical imagessuch as magnetic resonance images and computed tomography scan in bothtwo dimensions and in three dimensions. Further advantageously,Hierarchical Region Splitting does not require an atlas of normal ordiseased tissue for comparison, or depend on a probabilistic diseasemodel which are required by some other methods of analyzing medicalimages such as magnetic resonance images. Hierarchical Region Splittingcan be used for a variety of analyses, including detecting andquantifying an abnormality, such as for example an ischemic injury, to ahuman brain or other tissue, qualifying the abnormality, analyzing theinternal characteristics of the abnormality, quantifying thespatiotemporal evolution of the abnormality, as well as determining theeffects of endogenous or implanted neuronal stem cells (NSCs) on livinghuman tissue.

As used in this disclosure, except where the context requires otherwise,the term “comprise” and variations of the term, such as “comprising,”“comprises” and “comprised” are not intended to exclude other additives,components, integers or steps. Thus, throughout this disclosure, unlessthe context requires otherwise, the words “comprise,” “comprising” andthe like, are to be construed in an inclusive sense as opposed to anexclusive sense, that is to say, in the sense of “including, but notlimited to.”

As used in this disclosure, except where the context requires otherwise,the method steps disclosed and shown are not intended to be limiting norare they intended to indicate that each step is essential to the methodor that each step must occur in the order disclosed.

As using in this disclosure, “injury” includes both traumatic injury(such as for example gun shot wound) and non-traumatic injury (such asfor example ischemic stroke) as will be understood by those with skillin the art with reference to this disclosure.

As used in this disclosure, a “mask” of a region (such as “region A” ina sub-image) is a black-and-white image referred to as a “binary image”(usually of the same size as the original image), where a pixel in whitemeans that pixel is inside the region and a pixel in black means thatpixel is outside the region. This binary image, when superimposed on theoriginal medical image (Level 0 of the Hierarchical Region SplittingTree disclosed below), reveals the sub-image containing region A only.

As used in this disclosure, the term “machine readable medium” includes,but is not limited to portable or fixed storage devices, optical storagedevices, wireless channels and various other mediums capable of storing,containing or carrying either data, one or more than one instruction orboth data and one or more than one instruction.

As used in this disclosure, the term “computing device” includes, but isnot limited to computers, cellular telephones, hand-held computers andother devices that are capable of executing programmed instructions thatare contained in a storage including machine readable medium.

In the following disclosure, specific details are given to provide athorough understanding of the embodiments. However, it will beunderstood by one of ordinary skill in the art that the embodiments canbe practiced without these specific details. Well-known circuits,structures and techniques are not necessarily shown in detail in ordernot to obscure the embodiments. For example, circuits can be shown inblock diagrams in order not to obscure the embodiments in unnecessarydetail.

Also, some embodiments are disclosed as a process that is depicted as aflowchart, a flow diagram, a structure diagram, or a block diagram.Although a flowchart discloses the operations as a sequential process,many of the operations can be performed in parallel or concurrently. Inaddition, the order of the operations can be rearranged. A process isterminated when its operations are completed. A process can correspondto a method, a function, a procedure, a subroutine, a subprogram. When aprocess corresponds to a function, termination of the processcorresponds to a return of the function to the calling function or themain function.

Moreover, a storage can represent one or more devices for storing data,including read-only memory (ROM), random access memory (RAM), magneticdisk storage mediums, optical storage mediums, flash memory devices andother machine readable mediums for storing information.

Furthermore, embodiments can be implemented by hardware, software,firmware, middleware, microcode, or a combination of the proceeding.When implemented in software, firmware, middleware or microcode, theprogram code or code segments to perform the necessary tasks can bestored in a machine-readable medium such as a storage medium or otherstorage(s). One or more than one processor can perform the necessarytasks in series or in parallel. A code segment can represent aprocedure, a function, a subprogram, a program, a routine, a subroutine,a module, a software package, a class, or a combination of instructions,data structures, or program statements. A code segment can be coupled toanother code segment or a hardware circuit by passing or by receivinginformation, data, arguments, parameters, or memory contents.Information, arguments, parameters and data can be passed, forwarded, ortransmitted through a suitable means, such as for example memorysharing, message passing, token passing, network transmission.

According to one embodiment of the present invention, there is provideda method of analyzing a medical image comprising a region of interest,such as for example an image selected from the group consisting of acomputed tomography scan (CT scan), a magnetic resonance image (MRI), apositron emission tomography scan (PET scan) and an X-ray. In oneembodiment, the region of interest is a representation of an injury toliving human tissue and the method detects the representation of theinjury, however, the region of interest can be a representation of anyabnormality as will be understood by those with skill in the art withreference to this disclosure. In one embodiment, the region of interestcomprises a representation of an abnormality to living human tissue andthe method both detects the representation of the abnormality andqualifies the representation of the abnormality. In one embodiment, thehuman tissue is selected from the group consisting of heart, intestines,joints, kidneys, liver, lungs and spleen, though any suitable tissue canbe used as will be understood by those with skill in the art withreference to this disclosure. In a preferred embodiment, the livinghuman tissue is brain. The method will now be disclosed in greaterdetail with reference to magnetic resonance imaging of the living humanbrain as an example of the method, though corresponding steps can beused with other types of medical images and other types of human tissue,as will be understood by those with skill in the art with reference tothis disclosure.

According to one embodiment of the present invention, there is provideda method of analyzing a medical image. Referring now to FIGS. 1A and 1B,there are shown block diagrams showing some steps in a method accordingto the present invention of analyzing a medical image according to thepresent invention. As can be seen, in one embodiment, the methodcomprises first, providing a medical image. The medical image can beproduced specifically for the present method or can be previouslyproduced for another purpose and then used in the present method. Themedical image provided can be in a hard copy form (such as for example afilm or print form) or can be in a digital form. If the medical imageprovided is in a hard copy form, the method further comprises preparinga digital form of the medical image. The digital form of the medicalimage comprises a set of actual image values.

Next, the method comprises rescaling the actual image values to producecorresponding rescaled image values and a rescaled image from therescaled image values. In a preferred embodiment, the medical image is amagnetic resonance image and the actual image values are rescaled to fitin [0,255] unsigned 8-bit integer range; however, other ranges can beused as will be understood by those with skill in the art with referenceto this disclosure. The rescaling can be accomplished by any suitablemethod, as will be understood by those with skill in the art withreference to this disclosure. In one embodiment, rescaling isaccomplished using the following formula:

$\frac{\left( {{rscVal} - {\min{RSCVal}}} \right)}{\left. {{\max{RSCVal}} - {\min{RscVal}}} \right)} = \frac{\left( {{actVal} - {\min{ActVal}}} \right)}{\left( {{\max{ActVal}} - {\min{ActVal}}} \right)}$where minActVal is “minimum actual image value,” maxActVal is “maximumactual image value,” minRscVal is “minimum rescaled image value,” andmaxRscVal is “maximum rescaled image value.” As an example of therescaling step of the method, according to the present invention, theactual image values of a magnetic resonance image were rescaled togenerate a “rescaled image” (rscImg) by:

-   -   a) finding the “scale-factors” in the magnetic resonance image        (“Img”), where the scale factors consist of a “maximum image        value” (maxVal) and a “minimum image value” (minVal);    -   b) converting each “actual image value” (actVal) in the magnetic        resonance image to a corresponding “rescaled image value”        (rscVal), such as for example by using the formula:        rscVal=(actVal−minVal)/(maxVal−minVal)*255    -   thereby producing a group of rescaled image values;    -   c) saving the scale-factors for converting each rescaled image        value back to the corresponding actual image value; and    -   d) generating the rescaled image of the magnetic resonance image        using the group of rescaled image values.

Then, the method comprises deriving a histogram of the rescaled imagevalues. Deriving the histogram can be accomplished by:

-   -   a) determining the frequency that a particular rescaled image        value in the range [0,255] appears in the rescaled image; and    -   b) producing an array [rescaled image values, frequency] in the        form of a histogram (“H”) providing the following:        H(i)iε[1,N];where N=255

Next, the method comprises computing an adaptive segmentation threshold(also referred to as a “histogram shape-based image threshold”) that canbe used to split the gray level image of the rescaled image into twosub-images, a first sub-image with intensities at or below the adaptivesegmentation threshold and a second sub-image with intensities above theadaptive segmentation threshold, or alternately a first sub-image withintensities below the adaptive segmentation threshold and a secondsub-image with intensities at or above the adaptive segmentationthreshold, or alternately a first sub-image with intensities below theadaptive segmentation threshold and a second sub-image with intensitiesabove the adaptive segmentation threshold. The adaptive segmentationthreshold can be computed using any standard technique, as will beunderstood by those with skill in the art with reference to thisdisclosure. In a preferred embodiment, the adaptive segmentationthreshold is computed using Otsu's method, as will be understood bythose with skill in the art with reference to this disclosure. Insummary, the histogram H(i) is fit to a bimodal distribution (a functionwith two peaks), where each of the two peaks corresponds to a distinctcluster of image values from a relatively uniform region of the rescaledimage, and where each of the two peaks is distinct and separated enoughto be considered two different clusters. A valley (or trough) betweenthe two peaks is found that can separate the two peaks. The rescaledimage value of this valley is used as the adaptive segmentationthreshold (“Th”). Referring now to FIG. 2 and FIG. 3, there are shown,respectively, a histogram of a rescaled apparent diffusion coefficient(ADC) image plotting rescaled apparent diffusion coefficient imagevalues in the rescaled apparent diffusion coefficient image on the xaxis (in this case apparent diffusion coefficient image values) versusfrequency of each rescaled image value in the rescaled image on the yaxis (FIG. 2); and the corresponding table of rescaled apparentdiffusion coefficient image values for the rescaled image showing theadaptive segmentation threshold (Th) found for the histogram (FIG. 3).

The adaptive segmentation threshold (FIG. 3, right column) wasdetermined as follows:

-   -   a) the histogram (H(i)) was normalized to get the probabilistic        distribution function, (pdf, p(i));

${{p(i)} = {{H(i)}/{\sum\limits_{i = 1}^{N}{{H(i)}\mspace{14mu}{where}}}}},{N = 255}$

-   -   b) the cumulative distribution function cdf, ((i)) was found        that is cumulative sum of the pdf, p(I);

${\Omega(i)} = {\sum\limits_{j = 1}^{i}{p(j)}}$

-   -   c) the cumulative weighted means (μ(i)) of the pdf, p(i) was        found;

${\mu(i)} = {\sum\limits_{j = 1}^{i}{{p(j)}*j}}$

-   -   d) the final weighted mean (μt) was found;        μ_(t)=μ(N)        where, N=255    -   e) Otsu's measure (b2(i)) was computed using the formula:

${\sigma_{b}^{2}(i)} = \frac{\left\lbrack {{\mu_{t}*{\Omega(i)}} - {\mu(i)}} \right\rbrack^{2}}{\left\lbrack {{\Omega(i)}*\left( {1 - {\Omega(i)}} \right)} \right\rbrack}$

-   -   f) the mode (the number most frequently occurring in a series of        numbers; idx) was found in the series b2(i). If there were more        than one mode, the mean of modes were used:        idx=mean[modes(σ_(b) ²)]    -   g) the normalized threshold (the adaptive segmentation threshold        (Th), was found using the following formula:

${{Th} = {\frac{\left( {{idx} - 1} \right)}{\left( {N - 1} \right)}\mspace{14mu}{where}}},{N = 255}$where, N=255

Then, the method comprises recursively splitting the rescaled imageusing recursive bimodal segmentation to generate a hierarchical tree (a“Hierarchical Region Splitting Tree”) of sub(sub) images based onconsistency of the rescaled image values. Referring now to FIG. 4, thereis shown a schematic showing nomenclature rules for recursive splittingof a rescaled image, respectively. In a preferred embodiment, therescaled image is recursively split for example by:

-   -   a) using the adaptive segmentation threshold to split the        rescaled image into two sub-images: Lo_Img=(rscImg<Th) and        Hi_Img=(rscImg≧Th);    -   b) splitting the Lo_Img into 2 sub-sub-images using the steps        above (i. deriving a histogram, ii. computing an adaptive        segmentation threshold, and iii. splitting the rescaled image        using the adaptive segmentation threshold);    -   c) splitting the Hi_Img into 2 sub-sub-images using the steps        above (i. deriving a histogram, ii. computing an adaptive        segmentation threshold, and iii. splitting the rescaled image        using the adaptive segmentation threshold);    -   d) continuing to recursively split the sub-sub images generated        by steps b) and c) to generate a hierarchical structure of split        image regions using corresponding steps, and placing the images        obtained by recursive splitting in a Hierarchical Region        Splitting Tree.        The sub-images are named following the rules below:    -   Rule 1: Level 0: Image 0 is the entire rescaled image forming        the root of the Hierarchical Region Splitting Tree.    -   Rule 2: Level 1, sub-images: Image 1 is Lo_Img, one of the first        two recursively split images from image 0 (placed as the        left-leaf (left-child) in the Hierarchical Region Splitting        Tree). Image 2 is Hi_img, the other of the first two recursively        split images from image 0 (placed as the right-leaf        (right-child) in the Hierarchical Region Splitting Tree).    -   Rule 3: Level 2, sub-sub images: Image 11 and Image 12 are the        two recursively split images from Image 1 (placed as the        left-leaf (left-child) and the right-leaf (right-child) of Image        1, respectively, in the Hierarchical Region Splitting Tree) and        a right-child “12”; Image 21 and Image 22 are the two        recursively split images from Image 2 (placed as the left-leaf        (left-child) and the right-leaf (right-child) of Image 2,        respectively, in the Hierarchical Region Splitting Tree);    -   Rule 4: The Lo_Img=(rscImg<Th) for each subsequent recursively        split parent image is named Image [number(s) of the parent        followed by a “1”]; and the Hi_Img=(rscImg≧Th) for each        subsequent recursively split parent image is named Image        [number(s) of the parent followed by a “2”].

The further generated images continue to be split and named according toRule 4, as shown in FIG. 4, until predetermined split-terminationcriteria (disclosed below) are fulfilled.

Next, the method comprises terminating the recursive splitting of thesub(sub) images using one or more than one predetermined criterion, andthen identifying, preferably, one sub(sub) image in the terminatedHierarchical Region Splitting Tree which comprises the region ofinterest. In one embodiment, for example, the medical image provided isa magnetic resonance image of a human brain. Different types of braintissues are differently contrasted by magnetic resonance imaging.Therefore, brain regions with uniform rescaled image values are expectedto be from a single type of brain tissue. As an example, in a preferredembodiment, the magnetic resonance image provided is from human brain,and the following predetermined criteria, Criteria 1 or Criteria 2 areused to terminate the recursive splitting of the sub(sub) images to keepthe brain tissue regions of the images functionally meaningful and thesize of the Hierarchical Region Splitting Tree small:

-   -   Criteria 1: area threshold=50 pixels or approximately 2 ml in        volume, (AreaTh=50 pixels or approximately 2 ml in volume); when        the individual connected regions for a split image are small        (area <50 pixels or approximately 2 ml volume), the rescaled        image values are probably from a single brain region; or    -   Criteria 2: standard deviation threshold=10 rscVals (StdDevTh=10        rscVals) and kurtosis threshold=1.5 (KurtTh=1.5); when both the        rescaled image values have a low standard deviation (STD <10        rscVals) and the corresponding histogram for a split image has a        single very sharp peak (kurtosis <1.5); where the kurtosis image        value of a Gaussian Normal distribution is 3, the rescaled image        values are probably from a single brain region.

Referring now to FIG. 5, there is shown a diagram of the top levels of aHierarchical Region Splitting Tree generated by the method of thepresent invention for a magnetic resonance image which was generatedfrom the histogram of FIG. 2, where the level of image splitting isshown on the right side of the diagram beginning with the first image,Image 0, and the number of each sub-image and the range of rescaledimage values (v) present in the sub-image are shown below eachsub-image.

In one embodiment, the method further comprises performing a secondaryrescaling (a scaling back) of some or all of the rescaled sub-images inthe Hierarchical Region Splitting Tree generated from the medical imageprovided back to the actual image values present in the medical imageprovided to create secondary rescaled medical images. This secondaryrescaling of sub-images generates scaled-back sub-images having the samerange of image values as the range of image values present in themedical image provided and can be used to generate a Hierarchical RegionSplitting Tree using the actual image values. In one embodiment, thesecondary rescaling of each sub-image is performed using a formula thatis the counterpart of the formula in the rescaling step, as follows:actVal=[(rscVal/255)*(maxVal−minVal)]+minVal

According to another embodiment of the present invention, there isprovided a method of detecting an abnormality in living human tissue.The method comprises analyzing a medical image according to the presentinvention, where the medical image comprises a representation of theabnormality in the living human tissue. In one embodiment, the methodfurther comprises quantifying the abnormality in the living humantissue.

When imaged on a magnetic resonance image, injured brain tissues tend tohave signal image values in particular magnetic resonance imagemodalities that are distinct from non-injured brain tissues, andtherefore injured brain tissues tend to have good contrast with theadjacent non-injured brain tissues in magnetic resonance images, whichmakes detection and quantification of injured brain tissues frommagnetic resonance images using the present method particularlyeffective. For example, referring again to FIG. 5, using the method ofanalyzing a medical image as disclosed in this disclosure, a magneticresonance image of a living human brain was analyzed and the injuredbrain region that suffered an ischemic injury was detected as shown inImage 11 in level 2 as a hypo-intense ischemic injury region that isclearly visible in Image 11.

Referring now to FIG. 6, there is shown a block diagram showing somesteps in a method according to the present invention of detecting anabnormality in living human tissue using a magnetic resonance image asan example. As can be seen, the method of detecting an abnormality inliving human tissue comprises, first, analyzing a medical image asdisclosed in this disclosure, where the medical image comprises arepresentation of the abnormality in the living human tissue. The methodof detecting an abnormality in living human tissue will now be disclosedin further detail using a magnetic resonance image as an example of themedical image, and an injury to brain tissue as an example of anabnormality in the living human tissue; however, as will be understoodby those with skill in the art with reference to this disclosure, themedical image can be any suitable medical image, such as for example acomputed tomography scan (CT scan), a magnetic resonance image (MRI), apositron emission tomography scan (PET scan) and an X-ray, and theabnormality can be any suitable abnormality, such as for example agenetic malformation and an injury.

Next, the method comprises determining an image value (MeanTh) or a setof image values (MeanThs) of actual image values in a medical image(after the secondary rescaling back to the 1 mg image values), where theMeanTh or MeanThs determined identifies the abnormality represented inthe medical image, such as a region of brain tissue as injured braintissue, for the modality being used to generate the medical imageprovided, where the region of injured brain tissue has an actual imagevalue that is less than the MeanTh, greater than the MeanTh, or withinthe set of MeanThs, and where the MeanTh(s) comprises a type(s) and anamount(s). The MeanTh(s) are sometimes called a soft threshold imagevalue(s).

In one embodiment, determination of the MeanTh(s) is made before thestep of providing the medical image. In another embodiment,determination of the MeanTh(s) is made after the step of providing themedical image. In one embodiment, determining the MeanTh(s) comprisesconsulting references containing these MeanTh(s), as will be understoodby those with skill in the art with reference to this disclosure. Inanother embodiment, determining the MeanTh(s) comprises performingresearch to ascertain the MeanTh(s), as will be understood by those withskill in the art with reference to this disclosure. In one embodiment,the medical image is a magnetic resonance image and the modality beingused to generate the medical image provided is selected from the groupconsisting of an apparent diffusion coefficient (ADC) map, and amagnetic susceptibility map and a T2 map, though any suitable modalitycan be used as will be understood by those with skill in the art withreference to this disclosure. In one embodiment, the type of theMeanTh(s) determined is selected from the group consisting of diffusioncoefficient, magnetic susceptibility and T2 relaxation time, though anysuitable type of the MeanTh(s) can be used as will be understood bythose with skill in the art with reference to this disclosure.

Then, the method comprises selecting a “relational operator” selectedfrom the group consisting of “less than” (the MeanTh), “greater than”(the MeanTh), and “within” (the MeanThs), where the relation operatordetermined indicates the relationship of the MeanTh(s) determined to themean of the actual image values in the medical image (the secondarilyrescaled image values, that is, the rescaled image values after scalingback to actual image values) of the injured brain tissue for themodality being used to generate the medical image (in this example, amagnetic resonance image) provided. This relationship is used todetermine whether the tissues imaged in a sub-image of the medical imageconstitutes injured brain tissue for the medical image modality beingused.

Next, the method comprises comparing the MeanTh(s) to the average imagevalue of each of the scaled-back sub-images of the Hierarchical RegionSplitting Tree sequentially starting from the top level (level 0) of theHierarchical Region Splitting Tree and then downwards through level 1,level 2, and subsequent levels until reaching the first scaled-backsub-image “A” in the Hierarchical Region Splitting Tree that has anaverage actual magnetic resonance image value that satisfies therelational operator with the MeanTh(s), where the sub-image “A”comprises the abnormality (such as an ischemic injury) in the brain.

For example, from published works, in the modality of an apparentdiffusion coefficient (ADC) map for a magnetic resonance image, ischemicinjury to the brain has a MeanTh in rescaled apparent diffusioncoefficient image value of 80 and a relational operator of “less than.”Referring again to FIG. 5, as can be seen, Image ‘11’ has a mean imagevalue (for apparent diffusion coefficient) of 76, an image value that is“less than” the determined MeanTh of 80 and, therefore, Image ‘11’ isdetected as comprising an abnormality in the injured living human tissue(here ischemic injured brain tissue). (As can be appreciated, the actualadaptive threshold (95) used to create Image ‘11’ from Image 1 and anactual mean of this image sub-region (76) are different from the MeanTh(80) used to detect the abnormality in the living human tissue.)

For example, in ADC maps (magnetic resonance images) ofischemically-injured brain, when the mean ADC image value of ascaled-back ADC sub-image “A” is “less than” the MeanTh in ADC unit(mm/sec²), that sub-image “A” is delineated as ischemic injury based onthe magnetic resonance image modality of diffusion coefficients.Similarly, in T2 maps of is chemically-injured brain, when the mean T2relaxation time (millisecond) of a scaled-back T2 sub-image “B” is“greater than” the MeanTh (in milliseconds), then sub-image “B” isdelineated as ischemic injury based on the magnetic resonance imagemodality of T2 relaxation time.

When the relational operator is “within,” there are two MeanThs, and aninjury is determined to be present in the sub-image where the meanactual image value of a scaled-back sub-image is between the twoMeanThs. Determination that a scaled-back sub-image is between the twoMeanThs (MeanTh1 and MeanTh2) can be made by detecting the firstcomplementary sub image as follows (where MeanTh1=100 and MeanTh2=180 inthis example):

-   -   a) determining the mask (mask1) of the region for “less than”        MeanTh1;    -   b) determining the mask (mask2) of the region for “greater than”        MeanTh2;    -   c) determining the union of the masks [mask12=AND (mask1,        mask2)] from step a) and    -   d) this is the complementary region of what is being searched;    -   e) determining the mask (mask0) of the entire brain region from        Image ‘0’; and    -   f) determining the mask of the detected injury region        (injuryMask) by subtracting mask12 from mask0 (i.e.,        mask*=mask0−mask12).

In one embodiment, the method further comprises cleaning the mask of thedetected injury region (injuryMask) using morphological opening, closingand cleaning to remove small outlier regions to generate a cleanedinjuryMask, as will be understood by those with skill in the art withreference to this disclosure. In another embodiment, the method furthercomprises using the cleaned injuryMask for further morphologicalquantifications (such as for example, area/volume, 2D/3D shape, boundarycontour/surface, anatomical location, eigenvectors/image values,major/minor axes, orientation, compactness), as will be understood bythose with skill in the art with respect to this disclosure.

Referring now to FIG. 7 and FIG. 8, each Figure shows a histogram (left)of a rescaled image (plotting apparent diffusion coefficient rescaledimage values in the rescaled image on the x axis versus frequency ofeach rescaled image value in the rescaled image on the y axis) and thepart of the corresponding diagram (right) (Level 0, Level 1 and Level 3)of the levels of a Hierarchical Region Splitting Tree generated by themethod of the present invention for a magnetic resonance image which wasgenerated from the histogram (left), where the level of image splittingis shown on the right side of the diagram beginning with the firstimage, Image 0, and the range of rescaled image values present in thesub-image are shown below each sub-image, where FIG. 7 is generated fromthe brain of a human neonatal patient with a mild ischemic injury, andFIG. 8 is generated from the brain of a human neonatal patient with asevere ischemic injury. As can be seen, the abnormality is detected inthe left-most image of Level 3 in both diagrams because the left-mostimage of Level 3 is detected as the first sub-image that has a meanscaled-back image value (the scaled-back image value being the same asthe original diffusion coefficient image value) that satisfies therelational operator (“less than” in these examples) and the MeanTh (0.16mm2/sec diffusion for ischemic injury) previously determined.

Referring now to FIG. 9 and FIG. 10, each Figure shows a histogram(left) of a rescaled image (plotting rescaled image values for T2relaxation time in the rescaled image on the x axis versus frequency ofeach rescaled image value in the rescaled image on the y axis) and thepart of the corresponding diagram (right) (Level 0, Level 1 and Level 2)of the levels of a Hierarchical Region Splitting Tree generated by themethod of the present invention for a magnetic resonance image which wasgenerated from the histogram (left), where the level of image splittingis shown on the right side of the diagram beginning with the firstimage, Image 0, and the range of rescaled image values present in thesub-image are shown below each sub-image, where FIG. 9 is generated froma rat brain with a mild ischemic injury, and FIG. 10 is generated from arat brain with a severe ischemic injury. As can be seen, the abnormalityis detected in the right image of Level 1 in both diagrams because theright image of Level 1 is detected as the first sub-image that has amean scaled-back image value (the scaled-back image value being the sameas the original T2 relaxation time image value) that satisfies therelational operator (“greater than” in these examples) and the MeanTh(180 millisecond T2 relaxation time for ischemic injury) previouslydetermined.

Referring now to FIG. 11, there is shown a diagram comparing volumetricresults of magnetic resonance images of injured brains using methodsaccording to the present invention (HRS) and using standard manualmethods at different injury-severities. T2WI from mild (<15% lesion),moderate (15-35%) and severe (>35%) injuries are shown at the level ofthe horizontal line. The percentage of the lesion volume compared to theentire brain is shown below the 3D volumes. As can be seen, manuallydetected lesions in 2D (T2WI, row 1) and in 3D (row 2) and lesionsdetected according to the present invention (HRS) in 2D (T2WI, row 3)and in 3D (row 4) were similar between both methods, demonstrating thatresults using the present method (HRS) correlated accurately withresults using the standard manual method.

According to another embodiment of the present invention, there isprovided a method of detecting a core of an injury and detecting apenumbra of an injury in living human tissue, and distinguishing thecore from the penumbra. The method comprises analyzing the medical imageaccording to the present invention. In one embodiment, the human tissueis selected from the group consisting of heart, intestines, joints,kidneys, liver, lungs and spleen, though any suitable tissue can be usedas will be understood by those with skill in the art with reference tothis disclosure. In a preferred embodiment, the living human tissue isbrain. The method will now be disclosed in greater detail with referenceto magnetic resonance imaging of the living human brain as an example ofthe method, though corresponding steps can be used with other types ofmedical images and other types of human tissue, as will be understood bythose with skill in the art with reference to this disclosure.

The “core” of an injury is the area or volume that contains tissues thatare dead and completely irrecoverable. The “penumbra” of an injury isthe area or volume that contains tissue that is not dead but that isaffected by the injury, where some of the tissue is recoverable. Thepenumbra is generally located adjacent to or around the core. Outside ofthe core and penumbra are normal healthy tissues. The water content(such as for example as determined by T2 maps) and water mobility (suchas for example as determined by apparent diffusion coefficient maps) inmagnetic resonance images are generally different for the core ascompared to the penumbra, and the water content and water mobility inmagnetic resonance images are generally different for both the core andthe penumbra as compared to normal healthy tissues.

In one embodiment, the method according to the present invention ofdetecting a core of an injury and detecting a penumbra of an injury inliving human tissue, and distinguishing the core from the penumbra isaccomplished by:

-   -   a) detecting one sub(sub) image (designated the “injury        sub-image”) comprising the region of interest in the terminated        Hierarchical Region Splitting Tree, where the region of interest        represents the injury according to the present invention;    -   b) determining the mask of the injury (“InjuryMask”);    -   c) determining a sub-tree below the detected injury sub-image        using the injury sub-image as the root of the sub-tree;    -   d) determining the MeanThs (soft threshold image values), here        referred to as the “MeanThPnmb,” which is the image value for        separating the core from the penumbra by determining the water        content (such as for example as determined by T2 maps) and water        mobility (such as for example as determined by apparent        diffusion coefficient maps) in magnetic resonance images        associated with the core and the penumbra of a particular type        of injury from published sources or from expert knowledge;    -   e) comparing the MeanThPnmb inside the sub-tree to find the        penumbra and the mask of the penumbra (“PnmbMask”); and        determining the mask of the core (“CoreMask”) by subtracting the        PnmbMask from InjuryMask (that is, the CoreMask=the        InjuryMask−the PnmbMask). (Shown in red regions in FIG. 12 and        FIG. 13 where the Figures are in color and in the lighter gray        where the Figures are not in color).

For example, referring again to FIG. 5, Image 11 is the injury sub-imageand the sub-tree is Image 111 and Image 112 (as shown in FIG. 5), andsub-images below Image 111 and Image 112 (that are not shown in FIG. 5).Known T2 relaxation time image values for neonatal ischemic injury aregreater than 200 ms for the core, and 160 ms<T2 relaxation time <200 msfor the penumbra, and known apparent diffusion coefficient is less than0.25×10⁻³ mm²/sec for the core and 0.25×10⁻³ mm²/sec<apparent diffusioncoefficient <0.50×10⁻³ mm²/sec for the penumbra. Hence, using the imagevalues in step c) and the mean T2 relaxation time (“meanT2”) and/or meanapparent diffusion coefficient (“meanADC”) of a sub-image in thesub-tree from step b), the penumbra is decided by meanT2<MeanThPnmb=200ms, and by apparent diffusion coefficient meanADC>MeanThPnmb=0.25×10⁻³mm²/sec.

In one embodiment, the method further comprises determining differentgradations (quantitative measures) of the core and the penumbra usingsteps corresponding to other embodiments of the method disclosed in thisdisclosure as will be understood by those with skill in the art withreference to this disclosure. The different quantitative measures of thecore and the penumbra are useful for better pathological temporalmonitoring and therapeutic intervention, even if there is no scientificterm yet associated with the quantitative measures (such as core andpenumbra). In summary, the corresponding steps for determining thedifferent gradations of the core and penumbra are as follows:

-   -   a) providing the sub-tree of the HRS tree;    -   b) identifying small ranges in-between the entire signal range        of the injury sub-image in the root image that correspond to        corresponding gradations of the core and penumbra; and    -   c) computing the different gradations.

In one embodiment, the method further comprises clustering multiplesub-images with the same range (from different branches and differentlevels of the HRS tree) to get unified sub-region structures of theinjury.

Referring now to FIG. 12, there is shown a diagram of core-penumbrainjury detected according to the present invention in an animal brain.As can be seen, using the present method, core was detected as red areasin row 2, and penumbra was detected as blue areas in row 2 using T2 maps(row 1). Further, using the present method, even finer gradations of theinjury beyond the simple core-penumbra separation were identified in row3, where the red: T2>220; magenta: 200<T2<220; yellow: 190<T2<200; blue:180<T2<190; cyan: 170<T2<180; green: 150<T2<170; and white: 140<T2<150visually depict the finer gradations, where the Figures are in color andin varying shades of gray where the Figures are not in color.

Referring now to FIG. 13, there is shown a diagram of core-penumbrainjury detected according to the present invention in a human neonatalbrain. As can be seen, using the present method, core was detected asred areas and penumbra as blue areas in row 2 and row 5 using apparentdiffusion coefficient maps (row 1 and row 4). Further, using the presentmethod, even finer gradations of the injury beyond meanADC is meanapparent diffusion coefficient of a sub-image in the sub-tree from stepa) above and pseudo-colors used to visualize the finer gradation are asfollows:

a) red: meanADC<0.10×10⁻³ mm²/sec;

b) magenta: 0.10×10⁻³ mm²/sec<meanADC<0.20×10⁻³ mm²/sec;

c) yellow: 0.20×10⁻³ mm²/sec<meanADC<0.25×10⁻³ mm²/sec;

d) blue: 0.25×10⁻³ mm²/sec<meanADC<0.30×10⁻³ mm²/sec;

e) cyan: 0.30×10⁻³ mm²/sec<meanADC<0.35×10⁻³ mm²/sec;

f) green: 0.35×10⁻³ mm²/sec<meanADC<0.40×10⁻³ mm²/sec; and

g) white: 0.40×10⁻³ mm²/sec<meanADC<0.50×10⁻³ mm²/sec.

where the Figures are in color and in varying shades of gray where theFigures are not in color. The same techniques and range of image valuesare used for FIGS. 12 and 13, where only finer gradations of the injuryare shown for an injured animal brain and injured human brain,respectively.

As will be understood by those with skill in the art with reference tothis disclosure, injuries usually evolve spatially and temporally asdoes many other types of abnormalities such as genetic abnormality, thatis, the anatomical location and extent of an injury changes over timeafter the injury and usually involve an initial degeneration processfollowed by a recovery process. According to one embodiment of thepresent invention, there is provided a method of quantifying thespatiotemporal evolution of an injury in living human tissue. In oneembodiment, the method comprises:

-   -   a) using an established (preferably age-matched) atlas of the        injured tissue, such as for example an atlas of injured brain,        and a standard (manual or automatic) co-registration method to        overlap the two-dimensional or three-dimensional magnetic        resonance image onto the two-dimensional or three-dimensional        atlas of the injured tissue;    -   b) determining the anatomical regions involved in the injury at        different granular levels (such as for example the entire        injured region, the core and penumbra, or finer gradations of        the injured region);    -   c) quantifying different features specific to the spatial        overlaps; and    -   d) using longitudinal imaging data (such as for example        neuro-imaging data in the case of brain injury) to reveal        temporal variations of the spatial features in the quantifying        step.

By way of example only, such quantifying of the spatiotemporal evolutionof an injury comprises quantifying the mean T2 image value of theinjured tissues over time, how the volume of an injury changes overtime, and how an injured tissue recovers over time.

According to another embodiment of the present invention, there isprovided a method of detecting the effects of endogenous or implantedstem cells (such as for example neuronal stem cells (NSCs)) on livinghuman tissue. In one embodiment, the method involves the automateddetection and quantitative monitoring of stem cells. Stem cells thathave been labeled with iron are visible on various medical imagingtechniques, such as for example on magnetic resonance images in themodality of T2 maps and susceptibility weighted imaging (SWI) maps wherethe iron labeled stem cells appear as dark (hypo-intense) small clusterson the magnetic resonance image. Markers other than iron that allow stemcells to be distinguished from surrounding tissues are also useful forthis method, as will be understood by those with skill in the art withreference to this disclosure. In the present invention, the methodsdisclosed in this disclosure are used to monitor labeled stem cells. Inone embodiment, the method comprises:

-   -   a) determining the magnetic resonance image values of the        labeled and implanted stem cells, such as for example using the        published work or original research. For example, in T2 maps,        iron labeled neuronal stem cells outside of an injury usually        have pixels/voxels with a T2 relaxation time of less than 50 ms        (that is, the soft approximate threshold in T2 maps for iron        labeled neuronal stem cells is MeanThNSCout=50 ms for iron        labeled neuronal stem cells detection). Alternately, iron        labeled neuronal stem cells inside an injury can be different        from iron labeled neuronal stem cells outside of the injury due        to superimposition of injury-contrast and iron labeled neuronal        stem cells-contrast and voxel-averaging effect in the magnetic        resonance image, where ischemic injuries are bright        (hyper-intense) and iron-labeled NSCs are dark (hypo-intense) in        T2 maps. Hence, the corresponding approximate threshold the        “MeanTh NSCin” is determined by the equation:        meanThNSCin=meanThNSCout/meanNABM*meanInjury        where “meanNABM” and “meanInjury” are the actual mean T2 image        values of the normal area brain matter (NABM) and the injury,        respectively. This assumes that the contrast ratio between        iron-labeled neuronal stem cells and the surrounding tissues are        same in T2 maps, whether the neuronal stem cells are outside of        or inside the injury.    -   b) Neuronal Stem Cells Detection Outside Injury: From the        Hierarchical Region Splitting Tree generated according to the        present invention, the sub-regions with a mean MRI image value        less than MeanThNSC1 are found, and the mask of the detected        stem cells (nscMask) is found, again according to the present        invention. Referring now to FIG. 14, there is shown a diagram        depicting the detection of iron-labeled stem cells in an        ischemic animal brain over four weeks, where red is the ischemic        lesion, yellow is the iron-labeled murine neuronal stem cells,        where the Figures are in color and in varying shades of gray        where the Figures are not in color. As can be seen, the iron        labeled neuronal stem cells were detected in the left-most        strand (containing sub-images ‘0’-‘1’-‘11’-‘111’-‘1111’- and so        on) of the T2-map-based Hierarchical Region Splitting Tree near        the bottom of the entire tree (the entire HRS tree is not        shown).    -   c) Neuronal Stem Cells Detection Inside Injury: In this case,        HRS sub-tree is considered below the detected injury in the        magnetic resonance image, such as in T2 maps, and the sub-region        of the detected injury (injuryMask) is found that has mean        magnetic resonance image value less than the approximate        threshold “MeanThNSCin.” This sub-region of the injury is        identified as neuronal stem cells inside the injury. In general,        the neuronal stem cell regions are found at the left-most strand        of the sub-tree with detected injury as the root.    -   d) quantify different features morphological quantifications of        the “nscMask.” In a preferred embodiment, no morphological        cleaning is done as neuronal stem cells clusters are sometimes        very small in size. In one embodiment, the feature quantified is        selected from the group consisting of (actual) mean, anatomical        location, area/volume, 2D/3D shape, compactness, standard        deviation and weighted centroid.

Stem cells are attracted by signals from an injury region and the stemcells migrate, proliferate, differentiate and take part in recovery fromthe injury, including injury to the brain. Quantification of the stemcells' activities (in vivo) is currently performed by time-consuming andsubjective visual/manual methods. According to another embodiment of thepresent invention, there is provided a method of quantifyingspatiotemporal activities of implanted labeled stem cells in livinghuman tissue, including human brain. In one embodiment, the methodcomprises steps corresponding to steps from methods disclosed in thisdisclosure. In summary, shape, proximity and area-similarity basedmatching is done to track specific stem cells cluster over space andtime. Migrations of stem cells are computed by location changes of themagnetic resonance image-signal-weighted centroid of the same stem cellscluster over time. Direction, speed or both direction and speed can bedetermined. Proliferations of stem cells are computed by the expansionand compression of the area or volume of a particular stem cellscluster, where directional preferences in proliferation are computed bychanging shapes over time. Higher order statistics of the migration andproliferation (such as for example rate of change of migration and rateof change of proliferation) are also computed for detailed stem cellsactivities. Final locations of the stem cells are computed bydetermining the “leading edge,” that is, the farthest pixel/voxel of thestem cells cluster from the implantation site. As different stem cellsclusters take different paths towards the injury site, path-specificstem cells activities are quantified and compared for to allowmonitoring of stem cell therapy.

According to another embodiment of the present invention, there isprovided a method of quantifying the interaction between injuryevolution and stem-cell activities in living human tissue, includinghuman brain. In one embodiment, the method comprises steps correspondingto steps from methods disclosed in this disclosure.

Although the present invention has been discussed in considerable detailwith reference to certain preferred embodiments, other embodiments arepossible. Therefore, the scope of the appended claims should not belimited to the description of preferred embodiments contained in thisdisclosure. All references cited herein are incorporated by reference intheir entirety.

What is claimed is:
 1. A method of analyzing a medical image, themedical image comprising one or more than one region of interest, themethod comprising: a) providing the medical image comprising a set ofactual image values; b) rescaling the actual image values to producecorresponding rescaled image values and to produce a rescaled image fromthe rescaled image values; c) deriving a histogram of the rescaled imagevalues; d) using the histogram to derive an adaptive segmentationthreshold that can be used to split the rescaled image into twosub-images, a first sub-image with intensities at or below the adaptivesegmentation threshold and a second sub-image with intensities above theadaptive segmentation threshold, or a first sub-image with intensitiesbelow the adaptive segmentation threshold and a second sub-image withintensities at or above the adaptive segmentation threshold, or a firstsub-image with intensities below the adaptive segmentation threshold anda second sub-image with intensities above the adaptive segmentationthreshold; e) using the adaptive segmentation threshold to recursivelysplit the rescaled image to generate a Hierarchical Region SplittingTree of sub(sub) images based on consistency of the rescaled imagevalues of the rescaled image; f) terminating the recursive splitting ofthe sub(sub) images using one or more than one predetermined criteriathereby completing the Hierarchical Region Splitting Tree; and g)identifying one sub(sub) image in the terminated Hierarchical RegionSplitting Tree which comprises the region of interest; the methodfurther comprising performing a secondary rescaling of the rescaledimage values of every rescaled sub(sub) image in the Hierarchical RegionSplitting Tree back to the actual image values present in the medicalimage to create a secondary rescaled medical image, thereby producing asecondarily rescaled sub(sub) image comprising the region of interest;where the rescaled image values fit in [0,255] unsigned 8-bit integerrange; where the predetermined criteria is selected from the groupconsisting of area threshold=50 pixels and (standard deviationthreshold=10 rscVals (StdDevTh=10 rscVals) and kurtosis threshold=1.5);where the region of interest is a representation of an abnormality inthe living human tissue, and where the method further comprisesquantifying the abnormality in the living human tissue; where the methodfurther comprises performing a secondary rescaling of the rescaled imagevalues in every rescaled sub(sub) image in the Hierarchical RegionSplitting Tree back to the actual image values present in the medicalimage to create a secondary rescaled medical image, and determining animage value or a set of image values of actual image values in themedical image after the secondary rescaling, where the image value or aset of image values of actual image values determined identifies theabnormality represented in the medical image for the modality being usedto generate the medical image provided; and where the method furthercomprises preparing a mask of the sub(sub) image containing therepresentation of the abnormality, and cleaning the mask to remove smalloutlier regions to generate a cleaned mask of the sub(sub) imagecontaining the representation of the abnormality.
 2. The method of claim1, where the one sub(sub) image in the terminated Hierarchical RegionSplitting Tree comprising the region of interest is two-dimensional. 3.The method of claim 1, where the secondarily rescaled sub(sub) image inthe terminated Hierarchical Region Splitting Tree comprising the regionof interest is three-dimensional.
 4. The method of claim 1, where thesecondarily rescaled one sub(sub) image in the terminated HierarchicalRegion Splitting Tree comprising the region of interest istwo-dimensional.
 5. The method of claim 1, where the one sub(sub) imagein the terminated Hierarchical Region Splitting Tree comprising theregion of interest is three-dimensional.
 6. The method of claim 1, wherethe medical image is selected from the group consisting of a computedtomography scan, a magnetic resonance image, a positron emissiontomography scan and an X-ray.
 7. The method of claim 1, where the onesub(sub) image in the terminated Hierarchical Region Splitting Treecomprising the region of interest is two-dimensional.
 8. The method ofclaim 1, where the one sub(sub) image in the terminated HierarchicalRegion Splitting Tree comprising the region of interest isthree-dimensional.
 9. The method of claim 1, where the region ofinterest is a representation of an injury to living human tissue and themethod detects the representation of the injury.
 10. The method of claim9, where the method qualifies the representation of the injury.
 11. Themethod of claim 1, where the medical image is of human tissue selectedfrom the group consisting of brain, heart, intestines, joints, kidneys,liver, lungs and spleen.
 12. The method of claim 1, where the medicalimage provided is in a hard copy form, and where the method furthercomprises preparing a digital form of the medical image before providingthe medical image.
 13. The method of claim 1, where the abnormality isselected from the group consisting of a genetic malformation and aninjury.
 14. The method of claim 1, where determining the image value ora set of image values of actual image values is made before the step ofproviding the medical image.
 15. The method of claim 1, wheredetermining the image value or a set of image values of actual imagevalues is made after the step of providing the medical image.
 16. Themethod of claim 1, where the medical image is a magnetic resonance imageand the modality being used to generate the medical image provided isselected from the group consisting of an apparent diffusion coefficientmap, a magnetic susceptibility map and a T2 map.
 17. A method ofdetecting a core of an injury and detecting a penumbra of an injury inliving human tissue, and distinguishing the core from the penumbra, themethod comprising: a) detecting one sub(sub) image (the injurysub-image) in the terminated Hierarchical Region Splitting Treecomprising the region of interest according to claim 1, where the regionof interest represents the injury; b) determining the mask of theinjury; c) determining a sub-tree below the detected injury sub-imageusing the injury sub-image as the root of the sub-tree; d) determiningsoft threshold image values for separating the core from the penumbra;e) comparing the soft threshold image values inside the sub-tree to findthe penumbra and a mask of the penumbra; and f) determining the mask ofthe core by subtracting the mask of the penumbra from the mask of theinjury.
 18. The method of claim 17, further comprising determiningdifferent gradations of the core and the penumbra.
 19. The method ofclaim 1, further comprising quantifying the spatiotemporal evolution ofan injury in living human tissue.
 20. A method of detecting the effectsof endogenous or implanted stem cells on living human tissue, the methodcomprising: a) determining magnetic resonance image values of labeledand implanted stem cells; b) detecting the stem cells outside of theregion of interest using a method according to claim 1; and c) detectingthe stem cells inside of the region of interest using a method accordingto claim
 1. 21. The method of claim 19, further comprising quantifyingspatiotemporal activities of implanted labeled stem cells in the livinghuman tissue.
 22. A method of analyzing a medical image, the medicalimage comprising one or more than one region of interest, the methodcomprising: a) configuring at least one processor to perform thefunctions of: 1) providing the medical image comprising a set of actualimage values; 2) rescaling the actual image values to producecorresponding rescaled image values and to produce a rescaled image fromthe rescaled image values; 3) deriving a histogram of the rescaled imagevalues; 4) using the histogram to derive an adaptive segmentationthreshold that can be used to split the rescaled image into twosub-images, a first sub-image with intensities at or below the adaptivesegmentation threshold and a second sub-image with intensities above theadaptive segmentation threshold, or a first sub-image with intensitiesbelow the adaptive segmentation threshold and a second sub-image withintensities at or above the adaptive segmentation threshold, or a firstsub-image with intensities below the adaptive segmentation threshold anda second sub-image with intensities above the adaptive segmentationthreshold; 5) using the adaptive segmentation threshold to recursivelysplit the rescaled image to generate a Hierarchical Region SplittingTree of sub(sub) images based on consistency of the rescaled imagevalues of the rescaled image; 6) terminating the recursive splitting ofthe sub(sub) images using one or more than one predetermined criteriathereby completing the Hierarchical Region Splitting Tree; and 7)identifying one sub(sub) image in the terminated Hierarchical RegionSplitting Tree which comprises the region of interest; the methodfurther comprising performing a secondary rescaling of the rescaledimage values of every rescaled sub(sub) image in the Hierarchical RegionSplitting Tree back to the actual image values present in the medicalimage to create a secondary rescaled medical image, thereby producing asecondarily rescaled sub(sub) image comprising the region of interest;where the rescaled image values fit in [0,255] unsigned 8-bit integerrange; where the predetermined criteria is selected from the groupconsisting of area threshold=50 pixels and (standard deviationthreshold=10 rscVals (StdDevTh=10 rscVals) and kurtosis threshold=1.5);where the region of interest is a representation of an abnormality inthe living human tissue, and where the method further comprisesquantifying the abnormality in the living human tissue; where the methodfurther comprises performing a secondary rescaling of the rescaled imagevalues in every rescaled sub(sub) image in the Hierarchical RegionSplitting Tree back to the actual image values present in the medicalimage to create a secondary rescaled medical image, and determining animage value or a set of image values of actual image values in themedical image after the secondary rescaling, where the image value or aset of image values of actual image values determined identifies theabnormality represented in the medical image for the modality being usedto generate the medical image provided; where the medical image is amagnetic resonance image and the modality being used to generate themedical image provided is selected from the group consisting of anapparent diffusion coefficient map, a magnetic susceptibility map and aT2 map; and where the method further comprises preparing a mask of thesub(sub) image containing the representation of the abnormality, andcleaning the mask to remove small outlier regions to generate a cleanedmask of the sub(sub) image containing the representation of theabnormality.
 23. The method of claim 22, where the one sub(sub) image inthe terminated Hierarchical Region Splitting Tree comprising the regionof interest is two-dimensional.
 24. The method of claim 22, where thesecondarily rescaled sub(sub) image in the terminated HierarchicalRegion Splitting Tree comprising the region of interest isthree-dimensional.
 25. The method of claim 22, where the secondarilyrescaled one sub(sub) image in the terminated Hierarchical RegionSplitting Tree comprising the region of interest is two-dimensional. 26.The method of claim 22, where the one sub(sub) image in the terminatedHierarchical Region Splitting Tree comprising the region of interest isthree-dimensional.
 27. The method of claim 22, where the medical imageis selected from the group consisting of a computed tomography scan, amagnetic resonance image, a positron emission tomography scan and anX-ray.
 28. The method of claim 22, where the one sub(sub) image in theterminated Hierarchical Region Splitting Tree comprising the region ofinterest is two-dimensional.
 29. The method of claim 22, where the onesub(sub) image in the terminated Hierarchical Region Splitting Treecomprising the region of interest is three-dimensional.
 30. The methodof claim 22, where the region of interest is a representation of aninjury to living human tissue and the method detects the representationof the injury.
 31. The method of claim 30, where the method qualifiesthe representation of the injury.
 32. The method of claim 22, where themedical image is of human tissue is selected from the group consistingof brain, heart, intestines, joints, kidneys, liver, lungs and spleen.33. The method of claim 22, where the medical image provided is in ahard copy form, and where the method further comprises preparing adigital form of the medical image before providing the medical image.34. The method of claim 33, where the abnormality is selected from thegroup consisting of a genetic malformation and an injury.
 35. The methodof claim 33, where determining the image value or a set of image valuesof actual image values is made before the step of providing the medicalimage.
 36. The method of claim 33, where determining the image value ora set of image values of actual image values is made after the step ofproviding the medical image.
 37. The method of claim 33, where themedical image is a magnetic resonance image and the modality being usedto generate the medical image provided is selected from the groupconsisting of an apparent diffusion coefficient map, a magneticsusceptibility map and a T2 map.